source{d} paper reading club

Discuss papers at the intersection of Software Engineering, Programming Languages and Machine Learning communities related to applications of Machine Learning to Code.

Goal

promote reading papers
rise awareness on current research directions in PL, SW, ML communities
create opportunity for collaborations

How it works

Every 2 weeks we pick and discuss a paper.

Next paper is chosen from a list of candidates established at the end of the session.

Anyone can comment on a paper's PDF on GDrive with questions or things that are worth clarifying.

When

Every 2 weeks on Fridays at 4pm CET

Where

on-line on zoom, ID of the meeting is 974-346-848.
in source{d} office in Madrid

How to participate

propose/vote for a paper
join the session online, or in office space

How to organize

A description of the current organization workflow is maintained in organization-workflow.md.

Past papers

2019.11.29 CodeSearchNet Challenge: Evaluating the State of Semantic Code Search. (notes)
2019.11.15 When Deep Learning Met Code Search. (notes)
2019.10.18 Assessing the Generalizability of code2vec Token Embeddings. (notes)
2019.10.04 The Software Heritage Graph Dataset: Public Software Development Under One Roof. Antoine Pietri, first author of the paper joined us. (notes)
2019.09.20 End-to-end Deep Learning of Optimization Heuristics. Chris Cummins, author of the paper joined us. (notes)
2019.09.06 Topology Adaptive Graph Convolutional Networks. (notes)
2019.08.09 Attention Is All You Need. (notes)
2019.07.26 Aroma: Code Recommendation via Structural Code Search. (notes)
2019.07.12 XLNet: Generalized Autoregressive Pretraining for Language Understanding. (notes)
2019.06.28 Import2vec Learning Embeddings for Software Libraries. (notes)
2019.06.21 Cross-language clone detection by learning over abstract syntax trees. Daniel Perez, co-author of the paper joined us! (notes)
2019.06.14 Coloring Big Graphs with AlphaGoZero. (notes)
2019.05.31 Neural Networks for Modeling Source Code Edits and Learning to Represent Edits. (notes)
2019.05.17 Maybe Deep Neural Networks are the Best Choice for Modeling Source Code. (notes)
2019.05.03 skipped, due to long holidays (moved to the next slot).
2019.04.19 A Comprehensive Survey on Graph Neural Networks. (notes)
2019.04.05 How Powerful are Graph Neural Networks?. (notes)
2019.03.22 Generative Code Modeling with Graphs. (notes)
2019.02.22 The Adverse Effects of Code Duplication in Machine Learning Models of Code. (notes)
2019.02.08 Structured Neural Summarization. (notes)
2019.01.25 Sorting and Transforming Program Repair Ingredients via Deep Learning Code Similarities. Martin Monperrus and Matias Martinez, co-authors of the paper joined us! (notes)
2019.01.11 A general reinforcement learning algorithm that masters chess, shogi and Go through self-play. (notes)
2018.12.14 Improving Automatic Source Code Summarization via Deep Reinforcement Learning. (notes)
2018.11.30 Mining Change Histories for Unknown Systematic Edits. Tim Molderez, first author of the paper, joined us for this session! (notes)
2018.11.16 Deep Learning Type Inference. This time Earl T. Barr joined, one of the authors of the paper! (notes, meetup)
2018.11.02 Learning to Represent Edits. (notes)
2018.10.19 Relational inductive biases, deep learning, and graph networks. (notes)
2018.10.5: extra session, Code Vectors: Understanding Programs Through Embedded Abstracted Symbolic Traces. This time Jordan Henkel joined, one of the authors of the paper! (notes, slides)
2018.09.28: code2seq: Generating Sequences from Structured Representations of Code by Uri Alon, Eran Yahav and Omer Levy. (notes)
2018.09.14: Learning to Represent Programs with Graphs by Miltiadis Allamanis, Marc Brockschmidt and Mahmoud Khademi. (notes)
2018.08.31: Intelligent Code Reviews Using Deep Learning by Anshul Gupta and Neel Sundaresan. (notes)

All the past papers we studied are available in the reading club's GDrive.